{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#K-Means聚类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "前面几章我们介绍了监督学习，包括从带标签的数据中学习的回归和分类算法。本章，我们讨论无监督学习算法，聚类（clustering）。聚类是用于找出不带标签数据的相似性的算法。我们将介绍K-Means聚类思想，解决一个图像压缩问题，然后对算法的效果进行评估。最后，我们把聚类和分类算法组合起来，解决一个半监督学习问题。\n",
    "\n",
    "<!-- TEASER_END-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在*第一章，机器学习基础*中，我们介绍过非监督学习的目的是从不带标签的训练数据中挖掘隐含的关系。聚类，或称为聚类分析（cluster analysis）是一种分组观察的方法，将更具相似性的样本归为一组，或一类（cluster），同组中的样本比其他组的样本更相似。与监督学习方法一样，我们用n维向量表示一个观测值。例如，假设你的训练数据如下图所示：\n",
    "\n",
    "![cluster](mlslpic/6.1 cluster.png)\n",
    "\n",
    "聚类算法可能会分成两组，用圆点和方块表示，如下图所示：\n",
    "\n",
    "![twocluster](mlslpic/6.2 twocluster.png)\n",
    "\n",
    "也可能分成四组，如下图所示：\n",
    "\n",
    "![fourcluster](mlslpic/6.3 fourcluster.png)\n",
    "\n",
    "聚类通常用于探索数据集。社交网络可以用聚类算法识别社区，然后向没有加入社区的用户进行推荐。在生物学领域，聚类用于找出有相似模式的基因组。推荐系统有时也利用聚类算法向用户推荐产品和媒体资源。在市场调查中，聚类算法用来做用户分组。下面，我们就用K-Means聚类算法来为一个数据集聚类。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##K-Means聚类算法简介"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "由于具有出色的速度和良好的可扩展性，K-Means聚类算法算得上是最著名的聚类方法。K-Means算法是一个重复移动类中心点的过程，把类的中心点，也称重心（centroids），移动到其包含成员的平均位置，然后重新划分其内部成员。$K$是算法计算出的超参数，表示类的数量；K-Means可以自动分配样本到不同的类，但是不能决定究竟要分几个类。$K$必须是一个比训练集样本数小的正整数。有时，类的数量是由问题内容指定的。例如，一个鞋厂有三种新款式，它想知道每种新款式都有哪些潜在客户，于是它调研客户，然后从数据里找出三类。也有一些问题没有指定聚类的数量，最优的聚类数量是不确定的。后面我们会介绍一种启发式方法来估计最优聚类数量，称为肘部法则（Elbow Method）。\n",
    "\n",
    "K-Means的参数是类的重心位置和其内部观测值的位置。与广义线性模型和决策树类似，K-Means参数的最优解也是以成本函数最小化为目标。K-Means成本函数公式如下：\n",
    "\n",
    "$$J = \\sum_{k=1}^k\\sum_{i \\in {C_k}}{|x_i-\\mu_k|}^2$$\n",
    "\n",
    "$\\mu_k$是第$k$个类的重心位置。成本函数是各个类畸变程度（distortions）之和。每个类的畸变程度等于该类重心与其内部成员位置距离的平方和。若类内部的成员彼此间越紧凑则类的畸变程度越小，反之，若类内部的成员彼此间越分散则类的畸变程度越大。求解成本函数最小化的参数就是一个重复配置每个类包含的观测值，并不断移动类重心的过程。首先，类的重心是随机确定的位置。实际上，重心位置等于随机选择的观测值的位置。每次迭代的时候，K-Means会把观测值分配到离它们最近的类，然后把重心移动到该类全部成员位置的平均值那里。让我们用如下表所示的训练数据来试验一下：\n",
    "\n",
    "| 样本 | X0 | X1 |\n",
    "| :-: | :-: | :-: |\n",
    "| 1 | 7 | 5 |\n",
    "| 2 | 5 | 7 |\n",
    "| 3 | 7 | 7 |\n",
    "| 4 | 3 | 3 |\n",
    "| 5 | 4 | 6 |\n",
    "| 6 | 1 | 4 |\n",
    "| 7 | 0 | 0 |\n",
    "| 8 | 2 | 2 |\n",
    "| 9 | 8 | 7 |\n",
    "| 10 | 6 | 8 |\n",
    "| 11 | 5 | 5 |\n",
    "| 12 | 3 | 7 |\n",
    "\n",
    "上表有两个解释变量，每个样本有两个特征。画图如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "font = FontProperties(fname=r\"c:\\windows\\fonts\\msyh.ttc\", size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x9746b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "X0 = np.array([7, 5, 7, 3, 4, 1, 0, 2, 8, 6, 5, 3])\n",
    "X1 = np.array([5, 7, 7, 3, 6, 4, 0, 2, 7, 8, 5, 7])\n",
    "plt.figure()\n",
    "plt.axis([-1, 9, -1, 9])\n",
    "plt.grid(True)\n",
    "plt.plot(X0, X1, 'k.');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "假设K-Means初始化时，将第一个类的重心设置在第5个样本，第二个类的重心设置在第11个样本.那么我们可以把每个实例与两个重心的距离都计算出来，将其分配到最近的类里面。计算结果如下表所示：\n",
    "\n",
    "|样本|X0|X1|与C1距离|与C2距离|上次聚类结果|新聚类结果|是否改变|\n",
    "|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n",
    "|1|7|5|3.16 |2.00 |None|C2|YES|\n",
    "|2|5|7|1.41 |2.00 |None|C1|YES|\n",
    "|3|7|7|3.16 |2.83 |None|C2|YES|\n",
    "|4|3|3|3.16 |2.83 |None|C2|YES|\n",
    "|5|4|6|0.00 |1.41 |None|C1|YES|\n",
    "|6|1|4|3.61 |4.12 |None|C1|YES|\n",
    "|7|0|0|7.21 |7.07 |None|C2|YES|\n",
    "|8|2|2|4.47 |4.24 |None|C2|YES|\n",
    "|9|8|7|4.12 |3.61 |None|C2|YES|\n",
    "|10|6|8|2.83 |3.16 |None|C1|YES|\n",
    "|11|5|5|1.41 |0.00 |None|C2|YES|\n",
    "|12|3|7|1.41 |2.83 |None|C1|YES|\n",
    "|C1重心|4|6||||||\n",
    "|C2重心|5|5||||||\n",
    "\n",
    "新的重心位置和初始聚类结果如下图所示。第一类用X表示，第二类用点表示。重心位置用稍大的点突出显示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x97b37b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C1 = [1, 4, 5, 9, 11]\n",
    "C2 = list(set(range(12)) - set(C1))\n",
    "X0C1, X1C1 = X0[C1], X1[C1]\n",
    "X0C2, X1C2 = X0[C2], X1[C2]\n",
    "plt.figure()\n",
    "plt.title('第一次迭代后聚类结果',fontproperties=font)\n",
    "plt.axis([-1, 9, -1, 9])\n",
    "plt.grid(True)\n",
    "plt.plot(X0C1, X1C1, 'rx')\n",
    "plt.plot(X0C2, X1C2, 'g.')\n",
    "plt.plot(4,6,'rx',ms=12.0)\n",
    "plt.plot(5,5,'g.',ms=12.0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "现在我们重新计算两个类的重心，把重心移动到新位置，并重新计算各个样本与新重心的距离，并根据距离远近为样本重新归类。结果如下表所示：\n",
    "\n",
    "|样本|X0|X1|与C1距离|与C2距离|上次聚类结果|新聚类结果|是否改变|\n",
    "|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n",
    "|1|7|5|3.49 |2.58 |C2|C2|NO|\n",
    "|2|5|7|1.34 |2.89 |C1|C1|NO|\n",
    "|3|7|7|3.26 |3.75 |C2|C1|YES|\n",
    "|4|3|3|3.49 |1.94 |C2|C2|NO|\n",
    "|5|4|6|0.45 |1.94 |C1|C1|NO|\n",
    "|6|1|4|3.69 |3.57 |C1|C2|YES|\n",
    "|7|0|0|7.44 |6.17 |C2|C2|NO|\n",
    "|8|2|2|4.75 |3.35 |C2|C2|NO|\n",
    "|9|8|7|4.24 |4.46 |C2|C1|YES|\n",
    "|10|6|8|2.72 |4.11 |C1|C1|NO|\n",
    "|11|5|5|1.84 |0.96 |C2|C2|NO|\n",
    "|12|3|7|1.00 |3.26 |C1|C1|NO|\n",
    "|C1重心|3.80|6.40||||||\n",
    "|C2重心|4.57|4.14|||||||\n",
    "\n",
    "画图结果如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x996d5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C1 = [1, 2, 4, 8, 9, 11]\n",
    "C2 = list(set(range(12)) - set(C1))\n",
    "X0C1, X1C1 = X0[C1], X1[C1]\n",
    "X0C2, X1C2 = X0[C2], X1[C2]\n",
    "plt.figure()\n",
    "plt.title('第二次迭代后聚类结果',fontproperties=font)\n",
    "plt.axis([-1, 9, -1, 9])\n",
    "plt.grid(True)\n",
    "plt.plot(X0C1, X1C1, 'rx')\n",
    "plt.plot(X0C2, X1C2, 'g.')\n",
    "plt.plot(3.8,6.4,'rx',ms=12.0)\n",
    "plt.plot(4.57,4.14,'g.',ms=12.0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "我们再重复一次上面的做法，把重心移动到新位置，并重新计算各个样本与新重心的距离，并根据距离远近为样本重新归类。结果如下表所示：\n",
    "\n",
    "|样本|X0|X1|与C1距离|与C2距离|上次聚类结果|新聚类结果|是否改变|\n",
    "|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n",
    "|1|7|5|2.50 |5.28 |C1|C1|NO|\n",
    "|2|5|7|0.50 |5.05 |C1|C1|NO|\n",
    "|3|7|7|1.50 |6.38 |C1|C1|NO|\n",
    "|4|3|3|4.72 |0.82 |C2|C2|NO|\n",
    "|5|4|6|1.80 |3.67 |C1|C1|NO|\n",
    "|6|1|4|5.41 |1.70 |C2|C2|NO|\n",
    "|7|0|0|8.90 |3.56 |C2|C2|NO|\n",
    "|8|2|2|6.10 |0.82 |C2|C2|NO|\n",
    "|9|8|7|2.50 |7.16 |C1|C1|NO|\n",
    "|10|6|8|1.12 |6.44 |C1|C1|NO|\n",
    "|11|5|5|2.06 |3.56 |C2|C1|YES|\n",
    "|12|3|7|2.50 |4.28 |C1|C1|NO|\n",
    "|C1重心|5.50|7.00||||||\n",
    "|C2重心|2.20|2.80|||||||\n",
    "\n",
    "\n",
    "画图结果如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x9af3e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C1 = [0, 1, 2, 4, 8, 9, 10, 11]\n",
    "C2 = list(set(range(12)) - set(C1))\n",
    "X0C1, X1C1 = X0[C1], X1[C1]\n",
    "X0C2, X1C2 = X0[C2], X1[C2]\n",
    "plt.figure()\n",
    "plt.title('第三次迭代后聚类结果',fontproperties=font)\n",
    "plt.axis([-1, 9, -1, 9])\n",
    "plt.grid(True)\n",
    "plt.plot(X0C1, X1C1, 'rx')\n",
    "plt.plot(X0C2, X1C2, 'g.')\n",
    "plt.plot(5.5,7.0,'rx',ms=12.0)\n",
    "plt.plot(2.2,2.8,'g.',ms=12.0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "再重复上面的方法就会发现类的重心不变了，K-Means会在条件满足的时候停止重复聚类过程。通常，条件是前后两次迭代的成本函数值的差达到了限定值，或者是前后两次迭代的重心位置变化达到了限定值。如果这些停止条件足够小，K-Means就能找到最优解。不过这个最优解不一定是全局最优解。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "###局部最优解"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "前面介绍过K-Means的初始重心位置是随机选择的。有时，如果运气不好，随机选择的重心会导致K-Means陷入局部最优解。例如，K-Means初始重心位置如下图所示：\n",
    "\n",
    "![clusterlocaloptima](mlslpic/6.4 clusterlocaloptima.png)\n",
    "\n",
    "K-Means最终会得到一个局部最优解，如下图所示。这些类可能没有实际意义，而上面和下面两部分观测值可能是更有合理的聚类结果。为了避免局部最优解，K-Means通常初始时要重复运行十几次甚至上百次。每次重复时，它会随机的从不同的位置开始初始化。最后把最小的成本函数对应的重心位置作为初始化位置。\n",
    "\n",
    "![clusterlocaloptima2](mlslpic/6.5 clusterlocaloptima2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "###肘部法则"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果问题中没有指定$K$的值，可以通过肘部法则这一技术来估计聚类数量。肘部法则会把不同$K$值的成本函数值画出来。随着$K$值的增大，平均畸变程度会减小；每个类包含的样本数会减少，于是样本离其重心会更近。但是，随着$K$值继续增大，平均畸变程度的改善效果会不断减低。$K$值增大过程中，畸变程度的改善效果下降幅度最大的位置对应的$K$值就是肘部。下面让我们用肘部法则来确定最佳的$K$值。下图数据明显可分成两类："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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saJ5dxI6aeuNLkm5vzqRHKSKejohDkm6Q9BdjfbEg22+S9OuIOCfOniXppoh4haRbJL2n\nqUkvMcuCbnUTC8bH9rMk3SXpcxHx5dLz1CAifiPp65JeVXqWQv5M0lua7vVOSa+z/dnCMxUTEY80\nHx+TdLcmlfElZlnQZyX9se0V21dKOiLpqzP8PCwB25b0SUnnI+KO0vOUZPsFtq9tPn+OpDdIOld2\nqjIi4kMRcSAiXiLpbZLujYh3lJ6rBNtX276m+fy5kg5LuuwVYHte0BHxlKStm1jOS/riiJ+pv1PS\ndyUdtP2w7XeVnqmgmyT9jaTXNpcQnbN9c+mhCnmxpHubDvpBSfdExLcLz1SLMVekL5R0/7Z/L74W\nEd+83AO5UQUAKsVbXgFApVjQAFApFjQAVIoFDQCVYkEDQKVY0ABQKRY0AFSKBQ0Alfp/QLXJgVsb\n2YcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xace7908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "cluster1 = np.random.uniform(0.5, 1.5, (2, 10))\n",
    "cluster2 = np.random.uniform(3.5, 4.5, (2, 10))\n",
    "X = np.hstack((cluster1, cluster2)).T\n",
    "plt.figure()\n",
    "plt.axis([0, 5, 0, 5])\n",
    "plt.grid(True)\n",
    "plt.plot(X[:,0],X[:,1],'k.');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们计算$K$值从1到10对应的平均畸变程度："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7SZ8e4PVPSlq33kEknSVpsaRbB3i9T9LTkublx5cbC78hnprCzKyOejWDrYANgAMrGyQd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xac394e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial.distance import cdist\n",
    "K = range(1, 10)\n",
    "meandistortions = []\n",
    "for k in K:\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(X)\n",
    "    meandistortions.append(sum(np.min(cdist(X, kmeans.cluster_centers_, 'euclidean'), axis=1)) / X.shape[0])\n",
    "plt.plot(K, meandistortions, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('平均畸变程度',fontproperties=font)\n",
    "plt.title('用肘部法则来确定最佳的K值',fontproperties=font);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看出，$K$值从1到2时，平均畸变程度变化最大。超过2以后，平均畸变程度变化显著降低。因此肘部就是$K=2$。下面我们再用肘部法则来确定3个类的最佳的K值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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s1+SZb6MQEf8o6X9Oe/f1ku7v3r5f0m8udTvTWOK8EOgMurOO90j6et1JqvlTSX8o6Ye1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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9650e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x1 = np.array([1, 2, 3, 1, 5, 6, 5, 5, 6, 7, 8, 9, 7, 9])\n",
    "x2 = np.array([1, 3, 2, 2, 8, 6, 7, 6, 7, 1, 2, 1, 1, 3])\n",
    "X = np.array(list(zip(x1, x2))).reshape(len(x1), 2)\n",
    "plt.figure()\n",
    "plt.axis([0, 10, 0, 10])\n",
    "plt.grid(True)\n",
    "plt.plot(X[:,0],X[:,1],'k.');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TsdOBPaoyWwJzmhNBB+wPvFbilR0+TkREz+tGM9FqwPGSlqAknxNsnytpbwDbR9s+U9LO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\nZBARESQZREQE8P8BmtMYK7/TBJoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x98776a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial.distance import cdist\n",
    "K = range(1, 10)\n",
    "meandistortions = []\n",
    "for k in K:\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(X)\n",
    "    meandistortions.append(sum(np.min(cdist(X, kmeans.cluster_centers_, 'euclidean'), axis=1)) / X.shape[0])\n",
    "plt.plot(K, meandistortions, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('平均畸变程度',fontproperties=font)\n",
    "plt.title('用肘部法则来确定最佳的K值',fontproperties=font);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看出，$K$值从1到3时，平均畸变程度变化最大。超过3以后，平均畸变程度变化显著降低。因此肘部就是$K=3$。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##聚类效果评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们把机器学习定义为对系统的设计和学习，通过对经验数据的学习，将任务效果的不断改善作为一个度量标准。K-Means是一种非监督学习，没有标签和其他信息来比较聚类结果。但是，我们还是有一些指标可以评估算法的性能。我们已经介绍过类的畸变程度的度量方法。本节为将介绍另一种聚类算法效果评估方法称为轮廓系数（Silhouette Coefficient）。轮廓系数是类的密集与分散程度的评价指标。它会随着类的规模增大而增大。彼此相距很远，本身很密集的类，其轮廓系数较大，彼此集中，本身很大的类，其轮廓系数较小。轮廓系数是通过所有样本计算出来的，计算每个样本分数的均值，计算公式如下：\n",
    "\n",
    "$$s = \\frac {ba} {max(a,b)}$$\n",
    "\n",
    "$a$是每一个类中样本彼此距离的均值，$b$是一个类中样本与其最近的那个类的所有样本的距离的均值。下面的例子运行四次K-Means，从一个数据集中分别创建2，3，4，8个类，然后分别计算它们的轮廓系数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\programfiles\\Miniconda3\\lib\\site-packages\\numpy\\core\\_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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f6n2rKpdJR59Lipy5nXSk+gCw2yCv31G5TBqintWy/CCpm9D/tvzu/kI6Up9T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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbfeef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics\n",
    "\n",
    "plt.figure(figsize=(8, 10)) \n",
    "plt.subplot(3, 2, 1)\n",
    "x1 = np.array([1, 2, 3, 1, 5, 6, 5, 5, 6, 7, 8, 9, 7, 9])\n",
    "x2 = np.array([1, 3, 2, 2, 8, 6, 7, 6, 7, 1, 2, 1, 1, 3])\n",
    "X = np.array(list(zip(x1, x2))).reshape(len(x1), 2)\n",
    "plt.xlim([0, 10])\n",
    "plt.ylim([0, 10])\n",
    "plt.title('样本',fontproperties=font)\n",
    "plt.scatter(x1, x2)\n",
    "colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'b']\n",
    "markers = ['o', 's', 'D', 'v', '^', 'p', '*', '+']\n",
    "tests = [2, 3, 4, 5, 8]\n",
    "subplot_counter = 1\n",
    "for t in tests:\n",
    "    subplot_counter += 1\n",
    "    plt.subplot(3, 2, subplot_counter)\n",
    "    kmeans_model = KMeans(n_clusters=t).fit(X)\n",
    "    for i, l in enumerate(kmeans_model.labels_):\n",
    "        plt.plot(x1[i], x2[i], color=colors[l], marker=markers[l],ls='None')\n",
    "        plt.xlim([0, 10])\n",
    "        plt.ylim([0, 10])\n",
    "        plt.title('K = %s, 轮廓系数 = %.03f' % (t, metrics.silhouette_score(X, kmeans_model.labels_,metric='euclidean')),fontproperties=font)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "很显然，这个数据集包括三个类。在$K=3$的时候轮廓系数是最大的。在$K=8$的时候，每个类的样本不仅彼此很接近，而且与其他类的样本也非常接近，因此这时轮廓系数是最小的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##图像量化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "前面我们用聚类算法探索了结构化数据集。现在我们用它来解决一个新问题。图像量化（image quantization）是一种将图像中相似颜色替换成同样颜色的有损压缩方法。图像量化会减少图像的存储空间，由于表示不同颜色的字节减少了。下面的例子中，我们将用聚类方法从一张图片中找出包含图片大多数颜色的压缩颜色调色板（palette），然后我们用这个压缩颜色调色板重新生成图片。这个例子需要用`mahotas`图像处理库，可以通过`pip install mahotas`安装："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.utils import shuffle\n",
    "import mahotas as mh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先，读入图片，然后将图片矩阵展开成一个行向量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "original_img = np.array(mh.imread('mlslpic\\6.6 tree.png'), dtype=np.float64) / 255\n",
    "original_dimensions = tuple(original_img.shape)\n",
    "width, height, depth = tuple(original_img.shape)\n",
    "image_flattened = np.reshape(original_img, (width * height, depth))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后我们用K-Means算法在随机选择1000个颜色样本中建立64个类。每个类都可能是压缩调色板中的一种颜色。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=64, n_init=10,\n",
       "    n_jobs=1, precompute_distances='auto', random_state=0, tol=0.0001,\n",
       "    verbose=0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_array_sample = shuffle(image_flattened, random_state=0)[:1000]\n",
    "estimator = KMeans(n_clusters=64, random_state=0)\n",
    "estimator.fit(image_array_sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "之后，我们为原始图片的每个像素进行类的分配。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cluster_assignments = estimator.predict(image_flattened)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后，我们建立通过压缩调色板和类分配结果创建压缩后的图片："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CwoX1dZYXu0SDfR68tM4kLnng4gqqWa/kUguiyQSljGH2e1DBcMT21atEk4DJ/g47mzs8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zt3PqBsSbijDu6J+YKuGZSqFeozTTRdZf/BQx7eucuVK1e5ee8+smkgSwrh1CFOiqowTmJ0y0TW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NsyxTm4NtNxuqsiSKtFzeMkzSJME2TdQxFzUvCuqm1clcRYYaelzXYT6b4whXQ2mToswTmirj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YoYNBuKmOlk+k0h/8NQkyt+X9bTEkwV1usZSdEy6h2g1Stu5iGL9YZ+t8vRaMB0ueajzz4n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\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fu2Put9axsWmMfW6a5ZyfT/njP/5jE1SSppvM0eFwSBRFJEm6eX7yPN+wZdZ0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\nfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f608450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compressed_palette = estimator.cluster_centers_\n",
    "compressed_img = np.zeros((width, height, compressed_palette.shape[1]))\n",
    "label_idx = 0\n",
    "for i in range(width):\n",
    "    for j in range(height):\n",
    "        compressed_img[i][j] = compressed_palette[cluster_assignments[label_idx]]\n",
    "        label_idx += 1\n",
    "plt.subplot(122)\n",
    "plt.title('Original Image')\n",
    "plt.imshow(original_img)\n",
    "plt.axis('off')\n",
    "plt.subplot(121)\n",
    "plt.title('Compressed Image')\n",
    "plt.imshow(compressed_img)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##通过聚类学习特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "在下面的例子中，我们将聚类和分类组合起来研究一个半监督学习问题。你将对不带标签的数据进行聚类，获得一些特征，然后用这些特征来建立一个监督方法分类器。\n",
    "\n",
    "假设你有一只猫和一条狗。再假设你买了一个智能手机，表面上看是用来给人打电话的，其实你只是用来给猫和狗拍照。你的照片拍得很棒，因此你觉得你的朋友和同事一定会喜欢它们。而你知道一部分人只想看猫的照片，一部分人只想看狗的照片，但是要把这些照片分类太麻烦了。下面我们就做一个半监督学习系统来分别猫和狗的照片。\n",
    "\n",
    "回想一下*第三章，特征抽取与处理*的内容，有一个原始的方法来给图片分类，是用图片的像素密度值或亮度值作为解释变量。和我们前面进行文本处理时的高维向量不同，图片的特征向量不是稀疏的。另外，这个方法对图片的亮度，尺寸，旋转的变化都十分敏感。在*第三章，特征抽取与处理*里面，我们还介绍了SIFT和SURF描述器，用来描述图片的兴趣点，这类方法对图片的亮度，尺寸，旋转变化都不敏感。在下面的例子中，我们将用聚类算法处理这些描述器来学习图片的特征。每个元素将被编码成从图片中抽取的，被分配到同一个类的描述器的数量。这种方法有时也称为视觉词袋（bag-of-features）表示法，由于这个类的集合与词袋模型里的词汇表类似。我们将使用[Kaggle's Dogs vs. Cats competition](https://www.kaggle.com/c/dogs-vs-cats/data)里面的1000张猫图片和1000张狗图片数据。注意，图片有不同的尺寸；由于我们的特征向量不用像素表示，所有我们也不需要将所有图片都缩放成同样的尺寸。我们将训练其中60的图片，然后用剩下的40图片来测试："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import mahotas as mh\n",
    "from mahotas.features import surf\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import *\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "首先，我们加载图片，转换成灰度图，再抽取SURF描述器。SURF描述器与其他类似的特征相比，可以更快的被提取，但是从2000张图片中抽取描述器依然是很费时间的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_instance_filenames = []\n",
    "all_instance_targets = []\n",
    "for f in glob.glob('cats-and-dogs-img/*.jpg'):\n",
    "    target = 1 if 'cat' in f else 0\n",
    "    all_instance_filenames.append(f)\n",
    "    all_instance_targets.append(target)\n",
    "surf_features = []\n",
    "counter = 0\n",
    "for f in all_instance_filenames:\n",
    "    print('Reading image:', f)\n",
    "    image = mh.imread(f, as_grey=True)\n",
    "    surf_features.append(surf.surf(image)[:, 5:])\n",
    "    \n",
    "train_len = int(len(all_instance_filenames) * .60)\n",
    "X_train_surf_features = np.concatenate(surf_features[:train_len])\n",
    "X_test_surf_feautres = np.concatenate(surf_features[train_len:])\n",
    "y_train = all_instance_targets[:train_len]\n",
    "y_test = all_instance_targets[train_len:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后我们把抽取的描述器分成300个类。用`MiniBatchKMeans`类实现，它是K-Means算法的变种，每次迭代都随机抽取样本。由于每次迭代它只计算这些被随机抽取的一小部分样本与重心的距离，因此`MiniBatchKMeans`可以更快的聚类，但是它的畸变程度会更大。实际上，计算结果差不多："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_clusters = 300\n",
    "print('Clustering', len(X_train_surf_features), 'features')\n",
    "estimator = MiniBatchKMeans(n_clusters=n_clusters)\n",
    "estimator.fit_transform(X_train_surf_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "之后我们为训练集和测试集构建特征向量。我们找出每一个SURF描述器的类，用Numpy的`binCount()`进行计数。下面的代码为每个样本生成一个300维的特征向量："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = []\n",
    "for instance in surf_features[:train_len]:\n",
    "    clusters = estimator.predict(instance)\n",
    "    features = np.bincount(clusters)\n",
    "    if len(features) < n_clusters:\n",
    "    features = np.append(features, np.zeros((1, n_clusterslen(features))))\n",
    "    X_train.append(features)\n",
    "    \n",
    "X_test = []\n",
    "for instance in surf_features[train_len:]:\n",
    "    clusters = estimator.predict(instance)\n",
    "    features = np.bincount(clusters)\n",
    "    if len(features) < n_clusters:\n",
    "    features = np.append(features, np.zeros((1, n_clusterslen(features))))\n",
    "    X_test.append(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后，我们在特征向量和目标上训练一个逻辑回归分类器，然后估计它的精确率，召回率和准确率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = LogisticRegression(C=0.001, penalty='l2')\n",
    "clf.fit_transform(X_train, y_train)\n",
    "predictions = clf.predict(X_test)\n",
    "print(classification_report(y_test, predictions))\n",
    "print('Precision: ', precision_score(y_test, predictions))\n",
    "print('Recall: ', recall_score(y_test, predictions))\n",
    "print('Accuracy: ', accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "半监督学习系统仅仅使用像素密度作为特征向量，就获得了比逻辑回归分类器更好的精确率和召回率。而且，我们的特征向量只有300维，比100x100像素图片的10000维要小得多。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本章，我们介绍了我们的第一个无监督学习方法：聚类。聚类是用来探索无标签数据的结构的。我们介绍了K-Means聚类算法，重复将样本分配的类里面，不断的更新类的重心位置。虽然K-Means是无监督学习方法，其效果依然是可以度量的；用畸变程度和轮廓系数可以评估聚类效果。我们用K-Means研究了两个问题。第一个问题是图像量化，一种用单一颜色表示一组相似颜色的图像压缩技术。我们还用K-Means研究了半监督图像分类问题的特征。\n",
    "\n",
    "下一章，我们将介绍另一种无监督学习任务——降维（dimensionality reduction）。和我们前面介绍过的半监督猫和狗图像分类问题类似，降维算法可以在尽量保留信息完整性的同时，降低解释变量集合的维度。"
   ]
  }
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